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Comparative analysis of deep learning and machine learning techniques for forecasting new malaria cases in Cameroon's Adamaoua region

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Intelligence-Based Medicine 2025年 11卷 100220-100220页

作者： Naroum, Esaie Maka, Ebenezer Maka Abboubakar, Hamadjam Dayang, Paul Bamana, Appolinaire Batoure Garga, Benjamin Daouda, Hassana Daouda Bakouri, Mohsen Khan, Ilyas University of Ngaoundéré Department of Mathematics and computer Science Faculty of Science P.O. Box 455 Ngaoundéré Cameroon University of Douala National Higher Polytechnic School Department of Computer Engineering and Telecommunications P.O. Box 2701 Douala Cameroon University of Ngaoundéré University Institute of Technology Department of Computer Engineering P.O. Box 455 Ngaoundéré Cameroon University of Ngaoundéré Laboratory of Analysis Simulation and Test (LASE) P.O. Box 455 Ngaoundéré Cameroon University of Ngaoundéré School of Geology and Mining Engineering Department Applied Mathematics and Computer Science P.O. Box 115 Meiganga Cameroon University of Ngaoundéré ENSAI Department of Electrical Electronic and Automatic Engineering P.O. Box 455 Ngaoundéré Cameroon Majmaah University College of Applied Medical Science Department of Medical Equipment Technology Al-Majmaah 11952 Saudi Arabia Department of Mathematics College of Science Al-Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia

The Plasmodium parasite, which causes malaria is transmitted by Anopheles mosquitoes, and remains a major development barrier in Africa. This is particularly true considering the conducive environment that promotes the spread of malaria. This study examines several machine learning approaches, such as long short term memory (LSTM), random forests (RF), support vector machines (SVM), and data regularization models including Ridge, Lasso, and ElasticNet, in order to forecast the occurrence of malaria in the Adamaoua region of Cameroon. The LSTM, a recurrent neural network variant, performed the best with 76% accuracy and a low error rate (RMSE = 0.08). Statistical evidence indicates that temperatures exceeding 34 degrees halt mosquito vector reproduction, thereby slowing the spread of malaria. However, humidity increases the morbidity of the condition. The survey also identified high-risk areas in Ngaoundéré Rural and Urban and Meiganga. Between 2018 and 2022, the Adamaoua region had 20.1%, 12.3%, and 10.0% of malaria cases, respectively, in these locations. According to the estimate, the number of malaria cases in the Adamaoua region will rise gradually between 2023 and 2026, peaking in 2029 before declining in 2031. © 2025 The Authors

关键词： Adamaoua-Cameroon Deep learning Machine learning Malaria Metric evaluation Time-series forecasting

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Total Variation-Based Image Decomposition and Denoising for Microscopy Images

arXiv

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arXiv 2025年

作者： Corrias, Marco Franceschi, Giada Riva, Michele Tampieri, Alberto Föttinger, Karin Diebold, Ulrike Pock, Thomas Franchini, Cesare Faculty of Physics Center for Computational Materials Science University of Vienna Austria Vienna Doctoral School in Physics University of Vienna Austria Institute of Applied Physics TU Wien Austria Institute of Materials Chemistry TU Wien Austria Institute of Computer Graphics and Vision TU Graz Austria Department of Physics and Astronomy University of Bologna Italy

Experimentally acquired microscopy images are unavoidably affected by the presence of noise and other unwanted signals, which degrade their quality and might hide relevant features. With the recent increase in image acquisition rate, modern denoising and restoration solutions become necessary. This study focuses on image decomposition and denoising of microscopy images through a workflow based on total variation (TV), addressing images obtained from various microscopy techniques, including atomic force microscopy (AFM), scanning tunneling microscopy (STM), and scanning electron microscopy (SEM). Our approach consists in restoring an image by extracting its unwanted signal components and subtracting them from the raw one, or by denoising it. We evaluate the performance of TV-L1, Huber-ROF, and TGV-L1 in achieving this goal in distinct study cases. Huber-ROF proved to be the most flexible one, while TGV-L1 is the most suitable for denoising. Our results suggest a wider applicability of this method in microscopy, restricted not only to STM, AFM, and SEM images. The Python code used for this study is publicly available as part of AiSurf. It is designed to be integrated into experimental workflows for image acquisition or can be used to denoise previously acquired images. © 2025, CC BY.

关键词： Atomic force microscopy

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Predicting risk of cardiovascular disease using retinal optical coherence tomography imaging

arXiv

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arXiv 2024年

作者： Maldonado-Garcia, Cynthia Bonazzola, Rodrigo Ferrante, Enzo Julian, Thomas H. Sergouniotis, Panagiotis I. Ravikumar, Nishant Frangi, Alejandro F. Centre for Computational Imaging and Simulation Technologies in Biomedicine School of Computing University of Leeds Leeds United Kingdom CONICET-UNL Santa Fe Argentina Division of Evolution Infection and Genomics School of Biological Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom Manchester Royal Eye Hospital Manchester University NHS Foundation Trust Manchester United Kingdom Manchester Centre for Genomic Medicine Saint Mary’s Hospital Manchester University NHS Foundation Trust Manchester United Kingdom Wellcome Genome Campus Cambridge United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom School of Computer Science Faculty of Science and Engineering University of Manchester Kilburn Building Manchester United Kingdom Christabel Pankhurst Institute University of Manchester Manchester United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom

Cardiovascular diseases (CVD) are the leading cause of death globally. Non-invasive, cost-effective imaging techniques play a crucial role in early detection and prevention of CVD. Optical coherence tomography (OCT) has gained recognition as a potential tool for early CVD risk prediction, though its use remains underexplored. In this study, we investigated the potential of OCT as an additional imaging technique to predict future CVD events. We analysed retinal OCT data from the UK Biobank. The dataset included 612 patients who suffered a myocardial infarction (MI) or stroke within five years of imaging and 2,234 controls without CVD (total: 2,846 participants). A self-supervised deep learning approach based on Variational Autoencoders (VAE) was used to extract low-dimensional latent representations from high-dimensional 3D OCT images, capturing distinct features of retinal layers. These latent features, along with clinical data, were used to train a Random Forest (RF) classifier to differentiate between patients at risk of future CVD events (MI or stroke) and healthy controls. Our model achieved an AUC of 0.75, sensitivity of 0.70, specificity of 0.70, and accuracy of 0.70, outperforming the QRISK3 score (the third version of the QRISK cardiovascular disease risk prediction algorithm;AUC = 0.60, sensitivity = 0.60, specificity = 0.55, accuracy = 0.55). The choroidal layer in OCT images was identified as a key predictor of future CVD events, revealed through a novel model explainability approach. This study demonstrates that retinal OCT imaging is a cost-effective, non-invasive alternative for predicting CVD risk, offering potential for widespread application in optometry practices and hospitals. © 2024, CC BY-NC-ND.

关键词： Optical coherence tomography

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Assessing quality of decision reducts

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Procedia computer science 2020年 176卷 3273-3282页

作者： Urszula Stańczyk Beata Zielosko Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 41-200 Sosnowiec Poland

The paper presents research focused on decision reducts, a feature reduction mechanism inherent to rough sets theory. As a reduct enables to protect the discriminative properties of attributes with respect to described concepts, from the point of data representation, a reduct length is considered to be the most important measure of its quality. However, such approach is insufficient while taking into account the performance of a reduct-based rule classifier applied to test samples. When many reducts of the same length are available, they can lead to vastly different predictions. The paper provides a description for the proposed procedure for iterative reduct generation, which results in decrease of diversity in the observed levels of accuracy, supporting reduct selection. The procedure was applied for binary classification with balanced classes, for the stylometric task of authorship attribution.

关键词： reduct rough sets decision rules classification stylometry feature reduction

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Reduct-based ranking of attributes

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Procedia computer science 2020年 176卷 2576-2585页

作者： Beata Zielosko Urszula Stańczyk Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 41-200 Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting factor was defined, based on relative reducts. A reduct constitutes an embedded mechanism of feature selection, specific to rough set theory. The proposed factor takes into account the number of reducts in which a given attribute exists, as well as lengths of reducts. Two approaches for reduct generation were employed and compared, with search executed by a genetic algorithm. To validate the usefulness of the reduct-based rankings in the process of feature reduction, for gradually decreasing subsets of attributes, selected through rankings, sets of decision rules were induced in classical rough set approach. The performance of all rule classifiers was evaluated, and experimental results showed that the proposed rankings led to at least the same, or even increased classification accuracy for reduced sets of features than in the case of operating on the entire set of condition attributes. The experiments were performed on datasets from stylometry domain, with treating authorship attribution as a classification task, and stylometric descriptors as characteristic features defining writing styles.

关键词： feature reduction reduct ranking of attributes rough sets decision rules classification stylometry

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Sparse inpainting with smoothed particle hydrodynamics

arXiv

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arXiv 2020年

作者： Daropoulos, Viktor Augustin, Matthias Weickert, Joachim Interactive Graphics and Simulation Group Department of Computer Science Universität Innsbruck Technikerstraße 21a Innsbruck6020 Austria Mathematical Image Analysis Group Faculty of Mathematics and Computer Science Saarland University Campus E1.7 Saarbrücken66041 Germany

Digital image inpainting refers to techniques used to reconstruct a damaged or incomplete image by exploiting available image information. The main goal of this work is to perform the image inpainting process from a set of sparsely distributed image samples with the Smoothed Particle Hydrodynamics (SPH) technique. As, in its naive formulation, the SPH technique is not even capable of reproducing constant functions, we modify the approach to obtain an approximation which can reproduce constant and linear functions. Furthermore, we examine the use of Voronoi tessellation for defining the necessary parameters in the SPH method as well as selecting optimally located image samples. In addition to this spatial optimization, optimization of data values is also implemented in order to further improve the results. Apart from a traditional Gaussian smoothing kernel, we assess the performance of other kernels on both random and spatially optimized masks. Since the use of isotropic smoothing kernels is not optimal in the presence of objects with a clear preferred orientation in the image, we also examine anisotropic smoothing kernels. Our final algorithm can compete with well-performing sparse inpainting techniques based on homogeneous or anisotropic diffusion processes as well as with exemplar-based *** Codes 68U10 (Primary) 65D18, 76M28, 94A08 (Secondary) Copyright © 2020, The Authors. All rights reserved.

关键词： Hydrodynamics

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Performance evaluations of a cloud computing physical machine with task reneging and task resubmission (feedback) 27th

Performance evaluations of a cloud computing physical machin...

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27th International Conference on computer Networks, CN 2020

作者： Kuaban, Godlove Suila Soodan, Bhavneet Singh Kumar, Rakesh Czekalski, Piotr Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland School of Mathematics Shri Mata Vaishno Devi University KatraJammu and Kashmir182320 India Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9783030507183

Cloud service providers (CSP) provide on-demand cloud computing services, reduces the cost of setting-up and scaling-up IT infrastructure and services, and stimulates shorter establishment times for start-ups that offer or use cloud-based services. Task reneging or dropping sometimes occur when a task waits in the queue longer than its timeout or execution deadline, or it is compromised and must be dropped from the queue or as an active queue management strategy to avoid tail dropping of tasks when the queues are full. Reneged or dropped tasks could be resubmitted provided they were not dropped due to security reasons. In this paper, we present a simple M/M/c/N queueing model of a cloud computing physical machine, where the interarrival times and the services times are exponentially distributed, with N buffer size and c virtual machines running in parallel. We present numerical examples to illustrate the effect of task reneging and task resubmission on the queueing delay, probability of task rejection, and the probability of immediate service. © Springer Nature Switzerland AG 2020.

关键词： Cloud computing

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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ABCSS: A novel approach for increasing the TCP congestion window in a network

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Procedia computer science 2021年 191卷 437-444页

作者： N.M. Kasoro S.K. Kasereka G.K. Alpha K. Kyamakya University of Kinshasa Faculty of Sciences Mathematics and Computer Science Department Kinshasa Democratic Republic of the Congo Artificial intelligence Big data and modeLing simulation research center (ABIL) Kinshasa Democratic Republic of the Congo Université Libre des Pays des Grands Lacs Faculty of Sciences and Technologies Goma Democratic Republic of the Congo Alpen-Adria-Universitaet Klagenfurt Institute of Smart Systems Technologies Department of Mathematical Sciences Klagenfurt Austria

Congestion control is a useful task to be taken into account in order to maintain a good traffic in a network. In the case of a TCP/IP (Transmission Control Protocol/Internet Protocol) network, congestion creates queues in routers and even packet rejection. It is the reason why research have been conducted to improve congestion control mechanisms. Nowadays, several TCP congestion algorithms have been implemented. However, with the technology evolution and the need in terms of traffic capacity, various mechanisms implemented by these algorithms have shown their limitations and still need to be improved. The aim of this paper is to develop a novel TCP congestion control approach based on two famous algorithms: Appropriate Byte Counting (ABC) and Slow Start (SS). The results obtained show that ABC increases the congestion window appropriately compared to Slow Start. However, the congestion window remains invariant during the first round trips, which causes the TCP burst phenomenon. The TCP splitting can cause buffer overflows and long queues. For theses shortcomings, we propose ABCSS as a novel approach that appropriately increase the TCP congestion window and minimize the TCP splitting problem. The simulations of ABCSS algorithm provide interesting results compared to some known algorithms.

关键词： Congestion TCP ABC Slow Start Congestion window Congestion control ABCSS

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Unraveling the Role of Boron Dimers in the Electrical Anisotropy and Superconductivity in Boron-Doped Diamond

SSRN

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SSRN 2024年

作者： Sobaszek, Michal Kwon, Soonho Winiarski, Michal Rutkowski, Bogdan Ryl, Jacek Klimczuk, Tomasz Wang, Dongying Li, Xinwei Bockrath, Marc Bogdanowicz, Robert Goddard, William A. Gdańsk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Metrology and Optoelectronics 11/12 Narutowicza Str. 80-233 Poland Materials and Process Simulation Center California Institute of Technology 1200 East California Blvd. CA91125 United States Gdańsk University of Technology Faculty of Applied Physics and Mathematics and Advanced Materials Centre Department of Solid State Physics 11/12 Narutowicza Str. Gdańsk80-233 Poland Faculty of Metals Engineering and Industrial Computer Science AGH University of Krakow Al. A. Mickiewicza 30 Kraków30-059 Poland Advanced Materials Center Gdańsk University of Technology Narutowicza 11/12 Gdańsk80-233 Poland Department of Physics The Ohio State University ColumbusOH43210 United States

We use quantum mechanics (QM) to determine the states formed by B dopants in diamond. We find that isolated B sites prefer to form BB dimers and that the dimers pair up to form tetramers (BBCBB) that prefer to aggregate parallel to the (111) surface in the direction, one double layer below the H-terminated surface double layer. These tetramers lead to metallic character (Mott metal Insulator Transition) with holes in the valence band near the Γ point and electrons in the BBCBB tetramer promoted band along the X direction. Our experiments find very significant anisotropy in the superconductivity for boron-doped diamond thin films prepared with Microwave Plasma Assisted Chemical Vapor Deposition using deuterium-rich plasma. This leads to much higher conductivity in the X direction than the Y direction, as predicted by the QM. This phase transition to the anomalous phase is linked with the emergence of boson quantum entanglement states behaving as a bosonic insulating state. These anisotropic superconducting properties of the diamond film might enable applications such as single-photon detectors. We expect that this formation of a dirty superconductivity state is related to the BBCBB tetramers found in our QM calculations. © 2024, The Authors. All rights reserved.

关键词： Dimers

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